Examination of the Effects of Long-term COVID-19 Impacts on Patients with Neurological Disabilities Using a Neuromachine Learning Model

Deleted Journal, Journal Year: 2022, Volume and Issue: 1(1), P. 21 - 28

Published: Dec. 5, 2022

Currently, studies have shown that one in three people infected with coronavirus disease-19 (COVID-19) is likely to had long-term exposure COVID-19, known as COVID-19. Clinical indicate many the severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) COVID-19 pandemic exposure. According study, it has been said diabetes and obesity, who received organ transplants, are more suffer from this effect of In article, effects on neurological disability patients analyzed help a neuromachine learning model. The proposed model also shows COVID problem does not depend factors such race, age, gender, socioeconomic status those people. model, suffering problems continue physical fatigue shortness breath regularly monitored classified per instructions. Even after they recover disease, various side seen.

Language: Английский

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An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil

Mathematics, Journal Year: 2023, Volume and Issue: 11(14), P. 3069 - 3069

Published: July 12, 2023

This comprehensive overview focuses on the issues presented by pandemic due to COVID-19, understanding its spread and wide-ranging effects of government-imposed restrictions. The examines utility autoregressive integrated moving average (ARIMA) models, which are often overlooked in forecasting perceived limitations handling complex dynamic scenarios. Our work applies ARIMA models a case study using data from Recife, capital Pernambuco, Brazil, collected between March September 2020. research provides insights into implications adaptability predictive methods context global pandemic. findings highlight models’ strength generating accurate short-term forecasts, crucial for an immediate response slow down disease’s rapid spread. Accurate timely predictions serve as basis evidence-based public health strategies interventions, greatly assisting management. model selection involves automated process optimizing parameters autocorrelation partial plots, well various precise measures. performance chosen is confirmed when comparing forecasts with real reported after forecast period. successfully both recovered COVID-19 cases across preventive plan phases Recife. However, model’s observed extend future. By end period, error substantially increased, it failed detect stabilization deceleration cases. highlights challenges associated such under-reporting recording delays. Despite these limitations, emphasizes potential while emphasizing need further enhance long-term predictions.

Language: Английский

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An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 154, P. 106583 - 106583

Published: Jan. 25, 2023

Language: Английский

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Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods

Biology, Journal Year: 2023, Volume and Issue: 12(1), P. 117 - 117

Published: Jan. 11, 2023

Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk a myocardial infarction. Relations between factors CVDs are complex, ill-defined nonlinear, justifying use artificial intelligence tools. These tools aid in predicting classifying CVDs. In this article, we propose methodology using machine learning (ML) approaches predict, classify improve diagnostic accuracy CVDs, including support vector regression (SVR), multivariate adaptive splines, M5Tree model neural networks for training process. Moreover, neuro-fuzzy statistical approaches, nearest neighbor/naive Bayes classifiers inference system (ANFIS) used predict seventeen CVD factors. Mixed-data transformation classification methods employed categorical continuous variables risk. We compare our hybrid models existing ML techniques on real dataset collected from hospital. A sensitivity analysis performed determine influence exhibit essential with regard such as patient's age, cholesterol level glucose level. Our results report that proposed outperformed well known showing their versatility utility classification. investigation indicates prediction ANFIS process 96.56%, followed by SVR 91.95% accuracy. study includes comprehensive comparison obtained mentioned methods.

Language: Английский

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The Examination of the Effects of Long-term COVID-19 Impacts on Patients with Neurological Disabilities Using a Neuromachine Learning Model

Deleted Journal, Journal Year: 2023, Volume and Issue: 1(1), P. 22 - 29

Published: Jan. 1, 2023

Currently, studies have shown that one in three people infected with coronavirus disease-19 (COVID-19) is likely to had long-term exposure COVID-19, known as COVID-19. Clinical indicate many the severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) COVID-19 pandemic exposure. According study, it has been said diabetes and obesity, who received organ transplants, are more suffer from this effect of In article, effects on neurological disability patients analyzed help a neuromachine learning model. The proposed model also shows COVID problem does not depend factors such race, age, gender, socioeconomic status those people. model, suffering problems continue physical fatigue shortness breath regularly monitored classified per instructions. Even after they recover disease, various side seen.

Language: Английский

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Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries

Stochastic Environmental Research and Risk Assessment, Journal Year: 2022, Volume and Issue: 37(1), P. 345 - 359

Published: Oct. 5, 2022

Language: Английский

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Weibull Regression and Machine Learning Survival Models: Methodology, Comparison, and Application to Biomedical Data Related to Cardiac Surgery

Biology, Journal Year: 2023, Volume and Issue: 12(3), P. 442 - 442

Published: March 13, 2023

In this article, we propose a comparative study between two models that can be used by researchers for the analysis of survival data: (i) Weibull regression model and (ii) random forest (RSF) model. The are compared considering error rate, performance through Harrell C-index, identification relevant variables prediction. A statistical data set from Heart Institute University São Paulo, Brazil, has been carried out. study, length stay patients undergoing cardiac surgery, within operating room, was as response variable. obtained results show RSF less rate training testing sets, at 23.55% 20.31%, respectively, than model, which an 23.82%. Regarding obtain values 0.76, 0.79, models, respectively. After selection procedure, contains associated with type protocol patient being statistically significant 5%. chooses age, patient, We employ randomForestSRC package R software to perform our computational experiments. proposal present many applications in biology medicine, discussed conclusions work.

Language: Английский

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An Equity-Based Optimization Model to Solve the Location Problem for Healthcare Centers Applied to Hospital Beds and COVID-19 Vaccination

Mathematics, Journal Year: 2022, Volume and Issue: 10(11), P. 1825 - 1825

Published: May 26, 2022

Governments must consider different issues when deciding on the location of healthcare centers. In addition to costs opening such centers, three further elements should be addressed: accessibility, demand, and equity. Such locations chosen meet corresponding so that they guarantee a socially equitable distribution, ensure are accessible sufficient degree. The centers from set possible facilities certain minimum standards for operational viability Since potential does not necessarily cover demand all geographical zones, efficiency criterion maximized. However, efficient distribution resources equity criterion. Thus, decision-makers trade-off between these two criteria: described problem corresponds challenge governments face in seeking minimize impact pandemic citizens, where may either public hospitals care COVID-19 patients or vaccination points. this paper, we focus zone-divided region requiring localization We propose non-linear programming model solve based coverage formula using Gini index measure accessibility. Then, an approach epsilon constraints makes solvable with mixed integer linear computations at each iteration. A simulation algorithm is also considered generate instances, while computational experiments carried out show use proposed mathematical model. results spatial influences level system. Nevertheless, reduce inequity as supply health incorporated into decision-making process.

Language: Английский

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On a Novel Dynamics of SEIR Epidemic Models with a Potential Application to COVID-19

Symmetry, Journal Year: 2022, Volume and Issue: 14(7), P. 1436 - 1436

Published: July 13, 2022

In this paper, we study a type of disease that unknowingly spreads for long time, but by default, only to minimal population. This is not usually fatal and often goes unnoticed. We propose derive novel epidemic mathematical model describe such disease, utilizing fractional differential system under the Atangana–Baleanu–Caputo derivative. deals with transmission between susceptible, exposed, infected, recovered classes. After formulating model, equilibrium points as well stability feasibility analyses are stated. Then, present results concerning existence positivity in solutions sensitivity analysis. Consequently, computational experiments conducted discussed via proper criteria. From our experimental results, find loss regain immunity result gain infections. Epidemic models can be linked symmetry asymmetry from distinct view. By using approach, much research may expected epidemiology other areas, particularly COVID-19, state how develops after being infected virus.

Language: Английский

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Multi-strain COVID-19 dynamics with vaccination strategies: Mathematical modeling and case study

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 119, P. 665 - 684

Published: Feb. 12, 2025

Language: Английский

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